AMRC researchers Jide Oyebanji and Tarcisio Silvia will present papers at the MATLAB User Group Meeting in Abu Dhabi. Oyebanji's paper focuses on the 'Design of an Interactive TPMS Designing Desktop App' using MATLAB's numerical capabilities. Silvia's presentation discusses the optimization of MIMO active vibration controllers for electromechanical systems using MATLAB Simulink and Particle Swarm Optimization. Why it matters: The presentations showcase the application of computational tools like MATLAB in advanced materials research and digital engineering within the UAE.
Marcus Engsig from DERC will present a paper at the MATLAB User Group Meeting in Abu Dhabi on October 6. The paper, titled ‘Generalization of Higher Order Methods For Fast Iterative Matrix Inversion Compatible With GPU Acceleration’, discusses a novel approach to matrix inversion using GPUs. The method, named Nested Neumann, achieves 4-100x acceleration compared to standard MATLAB methods for large matrices. Why it matters: This research contributes to faster computation in numerical and physical modeling, crucial for processing large datasets in various scientific and engineering applications in the region.
KAUST's Visualization Core Lab (KVL) has released inshimtu, a pseudo in situ visualization system for scientists working with large datasets and supercomputer simulations. Inshimtu simplifies the implementation of in situ visualization by using existing simulation output files without requiring changes to the simulation code. It helps scientists determine if implementing a full in situ visualization into their code is worthwhile. Why it matters: This open-source tool can improve the efficiency of supercomputing research in the region by allowing researchers to assess the value of in situ visualization before fully committing to it.
Qirong Ho, co-founder and CTO of Petuum Inc., will be contributing to the "ML Systems for Many" initiative. Petuum is recognized for creating standardized building blocks for AI assembly. Ho also holds a Ph.D. from Carnegie Mellon University and is part of the CASL open-source consortium. Why it matters: Showcases the ongoing efforts to democratize AI development and deployment, making it more accessible and sustainable, although the specific initiative is not further detailed.
This paper introduces a virtual wheel-terrain interaction model developed and validated for the UAE Rashid rover to enhance simulation accuracy for space rovers. The model incorporates wheel grouser properties, slippage, soil properties, and interaction mechanics, validated via lunar soil simulation. Experiments tested a Grouser-Rashid rover wheel at slip ratios of 0, 0.25, 0.50, and 0.75. Why it matters: This simulation method advances rover design and control, crucial for the UAE's space exploration program and lunar mission success.
This paper presents the synthesis of a 1-DoF six-bar gripper mechanism for aerial grasping, designed for a task in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The synthesis process involves selecting the mechanism class, determining the number of links and joints using algebraic methods, and optimizing link dimensions via geometric programming. The gripper was modeled in CAD software, additively manufactured, and mounted on a UAV with a DC motor for gripping spherical objects. Why it matters: The research contributes to advancements in robotics and aerial manipulation, with potential applications in various industries, particularly for tasks requiring remote object retrieval and manipulation.
KAUST Ph.D. student Mohamed Bahloul received a best paper award at the IEEE Engineering in Medicine and Biology Society (EMBC ‘18) for the Africa and Middle East region. Bahloul's paper presented a three-element fractional-order viscoelastic Windkessel model developed in the EMAN group at KAUST. The model incorporates a fractional-order capacitor, potentially enabling earlier prediction of cardiovascular diseases. Why it matters: The award recognizes impactful research in biomedical engineering at KAUST and highlights the potential for advanced modeling techniques to improve healthcare in the region.
Researchers propose MS-NN-steer, a model-structured neural network for autonomous vehicle steering control that integrates nonlinear vehicle dynamics. The controller was validated using real-world data from the Abu Dhabi Autonomous Racing League (A2RL) competition. MS-NN-steer demonstrates improved accuracy, generalization, and robustness compared to general-purpose NNs and the A2RL winning team's controller. Why it matters: This research demonstrates a promising approach to developing transparent and reliable AI for safety-critical autonomous racing applications in the UAE.